1. Field of the Invention
The present invention relates generally to a low complexity near optimal two spatial stream maximal likelihood detector, and more particularly to a low complexity two spatial stream maximal likelihood detection method with the flexibility to limit processing throughput.
2. Description of the Related Art
A Multiple Input Multiple Output (MIMO) spatial multiplexing configuration significantly increases spectral efficiency: the gain is proportional to the number of spatial streams provided by the MIMO channel (i.e., the rank of the MIMO channel matrix). This technology has been adopted in both the IEEE 802.11n and 802.11ac WLAN standards, which provide high throughput with existing channelization bandwidth. However, to achieve this spectral efficiency gain, a linear MIMO receiver requires a higher signal-to-noise ratio (SNR) than non-spatial multiplexing receivers. In real environments, this translates to limited high throughput coverage, i.e. a station can only benefit from the use of spatial multiplexing in limited spots where scattering is rich and SNR is high.
Compared to linear detection, maximal likelihood (ML) detection can reduce the SNR requirement for MIMO detection. However, ML algorithm requires finding the nearest lattice coordinates, which is a NP problem with a complexity in the order of O(ML), where M is the size of the QAM set and L is the number of spatial streams, assuming equal modulation orders over all spatial streams. In the IEEE 802.11ac amendment, a new modulation order (256-QAM) is added, which dramatically increases the computation complexity for a ML detector. This change in the specification makes this detection method unduly difficult for real time implementation even for low rank (e.g., two spatial stream) spatial multiplexing.
Although the attempts to reduce ML detector complexity show that the ML detector complexity can be reduced to O(ML-1) without a loss of performance, this still results in very high complexity especially considering soft value output MLD is generally used in high order modulation. Similarly, sphere decoder methods have been shown to reduce the complexity by restricting the search in a sphere space centered at the initial Minimum Mean Error Square (MMSE) detection result, but, due to its depth first searching order, the number of candidates searched is variable. Breadth-first search algorithms, such as K-best, have a fixed number of candidates and hence a fixed computation throughput. Similarly to breadth-first algorithms, List detectors store visited candidate vectors in a list and a soft value can be derived from searching this list. In both sphere and List algorithms, the candidate sets are defined in the pre-transferred space. This does not guarantee that the complementary hypothesis for each bit position is included in the candidate set, which causes performance loss for both algorithms especially when the channel matrix is noticeably skewed.
Thus, there is a need for systems, devices, and methods for reducing ML detector complexity with an insignificant loss of performance.